Method and System for Automatic Lung Segmentation

ABSTRACT

Disclosed is a systematic way of automatically segmenting lung regions. To increase the efficiency of a lung segmentation technique, a region-based technique, such as region growing, is performed by a computer on a middle slice of the CT volume. A contour-based technique is then used for a plurality of non-middle slices of the CT volume. This allows the implementation to be multithreaded and results in an improvement in the segmentation algorithm&#39;s efficiency.

This application claims the benefit of U.S. Provisional Application No.60/1742,439, filed Dec. 5, 2005, which is incorporated herein byreference.

BACKGROUND OF THE INVENTION

Computer Axial Tomography (CAT), sometimes known generally asComputerized Tomography (CT), is used in many applications, especiallymedical radiology, to obtain two or three dimensional views of theinterior of three dimensional bodies (CT or CAT Scans). The techniqueinvolves subjecting a three dimensional body to radiation that entersthe body from many different angles. The amount of radiation that isscattered or reflected by the body is then detected as a function of theangle of scattering. The scattered data is then analyzed to construct animage of the interior of the body. A two dimensional “slice” of theinterior can be “reconstructed”, for example on a screen, and viewed.The slice can be reconstructed for any desired angle of intersectionwith the body.

While computerized tomography is well known, various specificmathematical reconstruction algorithms have been proposed to constructthe image from the scattered radiation. However, it is becoming morechallenging for known conventional reconstruction methods to meet thestringent constraints of current imaging applications. For example, therates at which the impinging radiation beam scans the body has increaseddramatically over the years, and the impinging radiation dosage hasdropped significantly, especially in medical applications, because ofpatient safety concerns.

One area in which CT scans can be used is to detect lung pathology(i.e., disease in one or both lungs). Typically, the lungs are segmentedfrom a CT scan in order to isolate the lungs from the rest of the image.Lung segmentation is typically a primary step for applications such aslung nodule detection and segmentation (i.e., the detection of andsegmentation of masses of tissues in the lung), lung registration (i.e.,automatic computation of the independent transformation of the right orleft lung from one dataset to another from the same patient. Thedatasets are typically acquired at two different time points),volumetric analysis and pathology analysis such as emphysema detection,etc.

FIG. 1 shows a prior art 2-D image 100 before segmentation. The imageincludes a background area 104, a chest wall 108 (consisting of ribs andmuscles), a right lung 112, and a left lung 116 The intensity level ofthe lungs is lower than that of the surrounding anatomies such as bones,muscles, and fat. A typical goal of lung segmentation is to extract theleft and right lungs 112, 116 from the image 100.

A number of methods have been developed to extract the lung regions froma CT image (also referred to as a volume). Some methods aresemi-automatic and involve user guidance. These methods often require aphysician to designate a seed point inside the CT image. From this seedpoint, a lung region is grown. The image may then be thresholded.Thresholding an image occurs when pixels of the image that have a greylevel higher (or lower) than a predetermined value, or threshold, isdesignated as being of interest, and the remaining pixels are designatedas not being of interest. A histogram of the grey levels of the imagemay be used to determine a threshold for the lungs within the image. Theresults of these semi-automatic methods can be unsatisfactory and mayrequire further manual corrections (e.g., because of the presence ofother regions of similar density. This could bias the value of theautomated threshold and, as a result, the lung may be over or undergrown).

Automatic lung segmentation techniques have also been developed.Typically, lung regions are separated from their surrounding tissuesbased on a threshold. The threshold can be predefined from empiricalresults and can be determined dynamically at run-time based on imagehistograms (as described above). An iterative method may also be used tofind a threshold.

In these techniques, algorithm speed is often affected by region-growingof the lungs. In particular, the growing of a lung region from a seedpoint is often computationally intensive and, as a result, the timerequired to perform the region growing is traditionally long relative toother operations.

Additionally, sometimes masses of tissues in the lung (i.e., lungnodules) are not included in a segmented lung region. In particular,some nodules touch the chest wall and possess the same intensity levelas the chest wall. After thresholding is performed on the image toextract the lung regions, the nodules may be excluded from the extractedlung regions. Further, lung nodules typically cannot be correctlyrecovered by a set of morphological operations. As a result, lungnodules are often improperly excluded from segmented lungs.

Therefore, there remains a need to more efficiently segment lung regionsfrom a CT image and further to accurately segment a lung having lungnodules.

BRIEF SUMMARY OF THE INVENTION

This invention provides a systematic technique of automaticallysegmenting lung regions. To increase the efficiency of a lungsegmentation technique, a region-based technique, such as regiongrowing, is first performed by a computer on the middle slice of the CTvolume. A contour-based technique is then used for a plurality ofnon-middle slices of the CT volume. This allows the implementation to bemultithreaded and results in an improvement in the segmentationalgorithm's efficiency

In one embodiment, tissues that are not related to the lungs are alsodetected (e.g., by comparing segmentation results from two consecutiveslices). The comparison of the segmentation results may contain a largeregion of high intensity pixels, which may be removed.

In one embodiment, the segmenting of the middle slice further includesapplying a median filter to the CT volume. The segmenting of the middleslice further includes removing non-body regions from the CT volume. Thesegmenting of the middle slice may also include locating a body regionin the CT volume. The segmenting of the middle slice further includeslabeling the lung regions. In one embodiment, the contour-basedtechnique further includes one or more of lung border tracing, lungsmoothing, and filling the lung regions.

Lung border tracing may include setting a starting point and a range tolimit searching of the starting point. The lung border tracing may alsoinclude executing a chain-code algorithm. The lung smoothing may alsoinclude performing a rolling-ball method.

In another embodiment, the lung smoothing further includes calculating acurvature of contour points determined by the contour-based technique.The curvature of a target point between a first point and a second pointis defined as:curvature=abs(θ2−θ1)÷dwhere d is the total length from said first point to said second point.

These and other advantages of the invention will be apparent to those ofordinary skill in the art by reference to the following detaileddescription and the accompanying drawings

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a prior art 2-D image including a background area, a chestwall (consisting of ribs and muscles), a right lung, and a left lung;

FIG. 2 is a flowchart illustrating the steps performed by a computer inaccordance with an embodiment of the present invention;

FIG. 3 is a flowchart illustrated the detailed steps performed by thecomputer to segment a middle slice of a CT scan in accordance with anembodiment of the present invention;

FIG. 4 is a lung region divided vertically into small regions inaccordance with an embodiment of the present invention;

FIG. 5 shows the definition of the directions in an 8-connectionneighborhood;

FIG. 6 shows a contour having a loop and a problem pixel;

FIG. 7 shows a contour having a dead-end;

FIG. 8 shows using the rolling-ball method on a contour in accordancewith an embodiment of the present invention;

FIG. 9 shows a block diagram of four points and a target point whosecurvature is being calculated in accordance with an embodiment of thepresent invention;

FIG. 10 shows a flowchart of the steps performed during a postprocessing step in accordance with an embodiment of the presentinvention;

FIG. 11 shows a high level block diagram of a computer in accordancewith an embodiment of the present invention; and

FIG. 12 shows segmentation results in accordance with an embodiment ofthe present invention.

DETAILED DESCRIPTION

In accordance with an embodiment of the present invention, a method andapparatus for automatically segmenting lung regions are described. Acontour-based method to extract lung regions in 2D includes performingregion growing in a “middle slice” of the CT volume. Segmentation may beexecuted on a slice-by-slice basis, thereby enabling multiple slices tobe segmented at substantially the same time.

FIG. 2 is a flowchart illustrating the steps performed by a computer inaccordance with an embodiment of the present invention. The computerfirst thresholds a CT image in step 205 using a predetermined threshold.Lung parenchyma tissues (i.e., the respiratory portions of the lung)have a density level in the range of −910 HU (i.e., Hounsfield units) to−500 HU, while the chest wall, vessel and bone are above this level. Inone embodiment, the predetermined threshold is set to −400 HU. Thisthreshold can be used to extract the lung regions (when segmenting amiddle slice, as described below) and may also be used as a criterionwhen judging whether a pixel belongs to the lung region or the chestwall.

As described in more detail below with respect to FIG. 3, the computerthen segments a “middle slice” of the CT volume in step 210. As usedherein, the “middle slice” of the CT volume refers to one (or more)slices that have a plurality of slices (e.g., the same number of slices)after and before the slice(s). The “middle slice” can therefore includeone or a plurality of slices that have approximately the same number ofslices before and after itself. Thus, the “middle slice” may refer tothe slice in the middle of a plurality of slices (e.g., slice 4 out of 7slices) or a slice that is the approximate middle of a plurality ofslices (e.g., slice 3 out of 6 slices).

Unlike typical lung segmentation algorithms, the computer performsregion labeling by a threshold on the middle slice of the CT volume. Asdescribed in more detail below, operations on other slices arecontour-based operations. Therefore, segmentations on two consecutiveslices are closely related. Further, the speed at which the algorithmexecutes improves because no three-dimensional region based algorithm isused.

The computer then segments the slices other than the middle slice instep 215. For slices other than the middle slice, the computer does notuse region-based techniques because these techniques are time consuming.Further, it is often difficult to differentiate between real lungregions of a CT image and other low intensity anatomies of the CT image.Instead, lung regions are extracted by using contour based operationswhich include lung border tracing, smoothing, and then filling the lungregions.

The computer then performs post processing in step 220. The postprocessing includes detecting large tissues that are enclosed in thesegmentation but are actually not lung tissues. In one embodiment, tosearch for such large tissues, segmentation results from two consecutiveslices are compared. If the difference contains a large region of highintensity pixels, then the large region is a target region and needs tobe removed. The computer then traces back to previous slices forsegments that belong to this tissue and removes them (i.e., the computergoes back to previous slices to check where the target region hasstarted in order to remove the target region from its beginning).

In one embodiment, the computer then performs right and left lungseparation in step 225. For example, the computer may separate the rightand left lungs for applications that depend on information specific toeach lung (e.g., follow-up study of lung nodules). In this situation,the two lungs have different labels in the segmentation. In oneembodiment, as the anterior and posterior junctions separating the leftand right lungs may be thin, and the intensity of these tissues may beat the same level as that of the lung regions, the two lungs may beconnected. In such a case, the computer can search in a particularregion around the anterior or posterior junction to locate the junctionline. The left and right lungs may then be separated by this line.

FIG. 3 is a flowchart illustrated the detailed steps performed by thecomputer to segment the middle slice (as introduced in step 210 of FIG.2). First, the computer applies a median filter in step 305. A mediafiler is an image processing tool. It computes the median value(different from the average) of a region of interest and assigns it tothe center of the region. It is typically used to uniform an image (getrid of a specific type of noise (e.g., speckle noise) that can be foundin medical images). Filtering may be performed on each image pixel withits eight direct neighbors. Specifically, for each pixel of the image,the median value of the pixel's 3×3 neighborhood is determined (3×3represents the size of the region of interest). The pixel is thenreplaced with the determined median value.

Non-body regions may then be removed in step 310. In particular, in someCT data, the regions outside the human body may have high intensitylevel. Before applying a threshold to separate lung regions from thechest wall, the high intensity regions that are outside the human bodyare removed. In one embodiment, a threshold value of 10000 HU isselected and the image is thresholded to remove these regions.

In step 315, the body region of the image is located. In one embodiment,the middle slice is labeled using a blob-coloring algorithm. Theblob-coloring algorithm is an algorithm that labels regions of the imagethat have intensity level above a pre-defined threshold. The largestregion is designated to be the body region that surrounds the two lungs.

The blob-coloring algorithm is typically efficient because the algorithmtraverses the entire image (i.e., slice) in a single pass and checks twopixel neighbors instead of eight. The blob algorithm finds the differentregions of an image and assigns them to a number in order of their“discovery”. It typically starts from the bottom left corner of theimage to the top right one. By doing so, it creates information aboutthe pixel it visits for the next ones, as of being part of a specificregion. Consequently, when checking a pixel, the algorithm needs toverify whether this current pixel is connected to previously detectedregions by checking the pixel on its left and below it. The differencewith a region growing algorithm is that the region growing startsanywhere within a region. It therefore needs to look all around to findits next neighbor (i.e., 8 pixels around instead of two for theblob-coloring algorithm).

The lung regions are then labeled in step 320. In one embodiment,locating the body region is performed to determine a range that containsthe lung(s) and to remove other, unrelated partitions that are outsidethe body. With this range defined, blob-coloring is performed again tofind the two (or one if they are connected) lung regions. This time, thegoal is to label regions with their intensity below the threshold. Withthe low intensity regions labeled, the largest two regions are selected.The size of a lung region on the middle slice has to be larger than apre-defined threshold. The sizes of the two selected regions arecompared with the threshold. If both are larger than the threshold, thenthe two lungs are separated on this slice. If only one region is largeenough, then the two lungs are connected. If neither of the two has avalid size, then this step is executed again on another slice until avalid lung region is obtained.

As described above, other slices are then segmented. As stated, forslices other than the middle slice, region growing is not used becauseregion growing is time consuming. Further, it is often difficult todifferentiate lung regions from other low intensity anatomies. Instead,lung regions are extracted by using contour-based operations. Thecontour-based operations can include lung border tracing, smoothing andfilling the lung regions.

Before the computer begins tracing, in one embodiment a predeterminednumber of parameters (e.g., two) are set. For example, a starting point,which may be the bottom-left point of the lung region in the slice, isset. Second, a range that limits the searching is set. The parametersmay be obtained from the segmentation result of the previous slice.

To determine the range that limits the searching, the range from aprevious slice is inherited and enlarged (e.g., by 50 pixels on each ofthe four directions (left, right, top, and bottom)).

In one embodiment, to ensure a satisfactory performance (e.g., in termsof speed) and segmentation consistency (with the result from theprevious slice), seed points are used to help locate the start pointsfor all lung regions in the current slice.

FIG. 4 shows a lung region 400 from a previous slice. The lung region400 from the previous slice is divided vertically into small regions asshown. In one embodiment, each small region is no less than 30 pixels inheight. A list of seed points is then constructed by extracting allcenter pixels (e.g., center pixel 404) of the sub-regions (e.g.,sub-region 408). The list is then sorted by pixel y-coordinates. Thecomputer then begins from the first point on the list and searchesdownwards until the region border. The bottom-left location of thesub-region is then recorded. This location can be used to start thetracing method.

For the other seeds left on the list, the computer checks if itsresiding sub-region has already been processed by the operationsoriginated from a previous seed. If not, then the seed is taken as a newstarting point and tracing begins again. In one embodiment, this routinecontinues until all seed points have been processed.

The computer then traces the lung borders. In particular, one outputfrom the previous step is the bottom-left pixel location of the lungregion being processed. This location is the start point of the tracing.A chain-code algorithm may then be used to trace lung borders. Achain-code algorithm is an algorithm that computes the contour of aregion based on the 8 neighbors of the starting pixel. The chain codealgorithm saves the direction of the next contour pixel and finally isrepresented by a starting point and a set of direction which compressessubstantially the format of the contour.

FIG. 5 shows the definition of the directions in an 8-connectionneighborhood 500. At a contour point, starting from the current tracingdirection, the computer searches counter-clockwise for the next contourpoint. If a contour point is found, the computer sets it as the currentcontour point and updates the new tracing direction as:New direction=(Current direction+5) modulo 8In one embodiment, the tracing continues until the tracing returns tothe original starting point.

In one embodiment, some contour points are crossed twice. FIG. 6 shows acontour 600 having a problem pixel 604. The problem pixel 604 is crossedtwice because of loop 608 (i.e., the problem pixel 604 is crossed onceon the way up the loop 608 and then again on the way down the loop 608).In this case, if the loop 608 is very small (e.g., less than 20 pixels),then this segment is removed from the contour. If the loop 608 is alarge segment, then the crossed pixel is marked and may be processeddifferently in the smoothing step described below.

FIG. 7 shows a contour 700 having a dead-end 704. If the computer istracing into dead-end 704, the tracing returns to the point where thedead-end 704 starts and then resumes normal tracing by adjusting a newtracing direction. Each pixel on the stick-like segment 704 is removedfrom the contour 700.

The lung borders are then smoothed. In particular, the lung borders mayhave some cavities that may be lung nodules. The computer may performthe smoothing step to recover these nodules that are attached to thechest wall. In one embodiment, a rolling-ball method is used to recoverthe nodules that are attached to the chest wall.

FIG. 8 shows using the rolling-ball method on a contour 800. In 2-D, therolling-ball method involves rolling a circle 804 along the contour 800(i.e., lung boundary). At contour indentation 808, the circle 804 spansover the cavity 812. A straight line 816 is then drawn between the twointersection points to fill up the cavity 812. In one embodiment, tworolling balls are used in the rolling-ball algorithm—one with fixed sizeand one with an adaptive diameter that depends on the length of thelocal contour.

In one embodiment, to save on computation time, curvatures of allcontour points are calculated first and only high curvature points areconsidered candidates to be smoothed. For example, when calculatingcurvature of a contour point, a window size of 13 pixels is chosen(e.g., 6 pixels on each side of the targeting point) and four pixelpoints are selected to calculate its curvature.

FIG. 9 shows a block diagram 900 of four points and a target point whosecurvature is being calculated. The block diagram 900 includes Point 1904, Point 2 908, Point 3 912, Point 4 916, and a Target point 920. Inone embodiment, Point 1 904 and Point 2 908 are the sixth and the thirdpoints to the Target point's left and Point 3 912 and Point 4 916 arethe third and sixth points to the Target point's right. The curvature ofthe Target Point 920 can be defined as:curvature=abs(θ2−θ1)÷dwhere d is the total length from Point 1 904 to Point 4 916. Thecomputer then fills the lung regions that are enclosed in the smoothingstep described above and the filled regions are the final lung regions.

FIG. 10 shows a flowchart of an embodiment of the steps performed duringthe post processing step (described above with respect to step 220 ofFIG. 2). The post processing step includes detecting large tissues thatare enclosed in the segmentation but are actually not lung tissues Tosearch for such large tissues, segmentation results from two consecutiveslices are compared and the difference is stored in a binary buffer instep 1005. The binary buffer is then labeled in step 1010. In oneembodiment, only high intensity pixels are labeled. Regions that arelarger than a defined threshold are then obtained in step 1015. If aregion larger than the defined threshold isn't found in step 1020, thenthe algorithm stops in step 1025.

If a region that is larger than the defined threshold is found in step1020, the computer performs a morphological opening. The morphologicalopening is a morphological operator used to smooth the region. Theregion is then removed from the segmentation result in step 1035. Theprevious slice is then set as the current slice in step 1040 and thealgorithm returns to step 1005.

The description herewith describes the present invention in terms of theprocessing steps required to implement an embodiment of the invention.These steps may be performed by an appropriately programmed computer,the configuration of which is well known in the art. An appropriatecomputer may be implemented, for example, using well known computerprocessors, memory units, storage devices, computer software, and othercomponents. A high level block diagram of such a computer is shown inFIG. 11. Computer 1102 contains a processor 1104 which controls theoverall operation of computer 1102 by executing computer programinstructions which define such operation. The computer programinstructions may be stored in a storage device 1112 (e.g., magneticdisk) and loaded into memory 1110 when execution of the computer programinstructions is desired. Computer 1102 also includes one or moreinterfaces 1106 for communicating with other devices (e.g., locally orvia a network). Computer 1102 also includes input/output 1108 whichrepresents devices which allow for user interaction with the computer1102 (e.g., display, keyboard, mouse, speakers, buttons, etc.).

One skilled in the art will recognize that an implementation of anactual computer will contain other components as well, and that FIG. 11is a high level representation of some of the components of such acomputer for illustrative purposes. In addition, one skilled in the artwill recognize that the processing steps described herein may also beimplemented using dedicated hardware, the circuitry of which isconfigured specifically for implementing such processing steps.Alternatively, the processing steps may be implemented using variouscombinations of hardware and software. Also, the processing steps maytake place in a computer or may be part of a larger machine.

Thus, a contour-based approach is used to segment the lung regions. Thecontour-based approach is a fully automatic method and typicallyperforms much faster than a region-based approach. In accordance with anembodiment of the present invention, lung regions are segmented as awhole and each individual anatomic region (e.g., trachea, bronchi,vessel, lung lobe, etc.) are not extracted individually. The algorithmcan be implemented in a lung nodule detection system since it does notrequire individual segmentation and because of the algorithm's treatmenton chest wall attaching nodules. The segmented lung can also be used inmany applications, such as in lung cancer screening, lung registration,and other lung pathology analysis. The algorithm can be applied inapplications that have higher performance requirements.

FIG. 12 shows segmentation results using the algorithm described above.In particular, the top row 1204 shows the original CT image and thebottom row shows the segmented lungs 1208 (segmented using the algorithmdescribed above).

The foregoing Detailed Description is to be understood as being in everyrespect illustrative and exemplary, but not restrictive, and the scopeof the invention disclosed herein is not to be determined from theDetailed Description, but rather from the claims as interpretedaccording to the full breadth permitted by the patent laws. It is to beunderstood that the embodiments shown and described herein are onlyillustrative of the principles of the present invention and that variousmodifications may be implemented by those skilled in the art withoutdeparting from the scope and spirit of the invention. Those skilled inthe art could implement various other feature combinations withoutdeparting from the scope and spirit of the invention.

1. A method of segmenting lungs from a Computerized Tomography (CT)volume comprising a plurality of slices, said method comprising:thresholding said CT volume using a predetermined threshold; segmentinga middle slice of said CT volume using a region-based technique; andsegmenting a plurality of non-middle slices of said CT volume using acontour-based technique.
 2. The method of claim 1 further comprisingdetecting tissues that are not related to said lungs.
 3. The method ofclaim 2 further comprising comparing segmentation results from twoconsecutive slices.
 4. The method of claim 3 further comprisingdetermining if the comparison of said segmentation results contains alarge region of high intensity pixels.
 5. The method of claim 4 furthercomprising removing said large region of high intensity pixels.
 6. Themethod of claim 1 wherein said segmenting of said middle slice furthercomprises applying a median filter to said CT volume.
 7. The method ofclaim 1 wherein said segmenting of said middle slice further comprisesremoving non-body regions from said CT volume.
 8. The method of claim 1wherein said segmenting of said middle slice further comprises locatinga body region in said CT volume.
 9. The method of claim 1 wherein saidsegmenting of said middle slice further comprises labeling said lungregions.
 10. The method of claim 1 wherein said contour-based techniquefurther comprises at least one of lung border tracing, lung smoothing,and filling lung regions.
 11. The method of claim 10 wherein said lungborder tracing further comprises setting a starting point and a range tolimit searching.
 12. The method of claim 11 wherein said lung bordertracing further comprises executing a chain-code algorithm.
 13. Themethod of claim 10 wherein said lung smoothing further comprisesperforming a rolling-ball method.
 14. The method of claim 10 whereinsaid lung smoothing further comprises calculating a curvature of contourpoints determined by said contour-based technique.
 15. The method ofclaim 14 wherein said curvature of a target point between a first pointand a second point is defined ascurvature=abs(θ2−θ1)÷d where d is the total length from said first pointto said second point.
 16. An apparatus for segmenting lungs from aComputerized Tomography (CT) volume comprising a plurality of slices,said apparatus comprising: means for thresholding said CT volume using apredetermined threshold; means for segmenting a middle slice of said CTvolume using a region-based technique; and means for segmenting theother slices of said CT volume using a contour-based technique.
 17. Theapparatus of claim 16 wherein said means for segmenting said middleslice further comprises means for applying a median filter to said CTvolume.
 18. The apparatus of claim 16 wherein said means for segmentingsaid middle slice further comprises means for removing non-body regionsfrom said CT volume.
 19. The apparatus of claim 16 wherein said meansfor segmenting said middle slice further comprises means for locating abody region in said CT volume.
 20. The apparatus of claim 16 whereinsaid means for segmenting said middle slice further comprises means forlabeling said lung regions.
 21. The apparatus of claim 16 wherein saidcontour-based technique further comprises means for at least one of lungborder tracing, lung smoothing, and filling lung regions.
 22. Theapparatus of claim 21 wherein said means for lung border tracing furthercomprises means for executing a chain-code algorithm.
 23. The apparatusof claim 21 wherein said means for lung smoothing further comprisesmeans for performing a rolling-ball method.
 24. The apparatus of claim23 wherein said means for lung smoothing further comprises means forcalculating a curvature of contour points determined by saidcontour-based technique.
 25. A computer readable medium comprisingcomputer program instructions capable of being executed in a processorand defining the steps comprising: thresholding a CT volume using apredetermined threshold; segmenting a middle slice of said CT volumeusing a region-based technique; and segmenting the other slices of saidCT volume using a contour-based technique.
 26. The computer readablemedium of claim 25 further comprising the step of detecting tissues thatare not related to said lungs.
 27. The computer readable medium of claim25 wherein the step of segmenting said middle slice further comprisesthe step of applying a median filter to said CT volume.
 28. The computerreadable medium of claim 25 wherein the step of segmenting said middleslice further comprises the step of removing non-body regions from saidCT volume.
 29. The computer readable medium of claim 25 wherein the stepof segmenting said middle slice further comprises the step of locating abody region in said CT volume.
 30. The computer readable medium of claim25 wherein the step of segmenting said middle slice further comprisesthe step of labeling said lung regions.
 31. The computer readable mediumof claim 25 wherein said contour-based technique further comprises atleast one of lung border tracing, lung smoothing, and filling lungregions.
 32. The computer readable medium of claim 31 wherein said lungsmoothing further comprises calculating a curvature of contour pointsdetermined by said contour-based technique.